class_docstr: Gaussian Fourier embeddings for noise levels.
class_docstr_tok: ["Gaussian","Fourier","embeddings","for","noise","levels","."]
code: def __init__(self, embedding_size=256, scale=1.0):
      super().__init__()
      self.register_buffer(
          "weight", paddle.randn((embedding_size,)) * scale
      )
  
      # to delete later
      self.register_buffer("W", paddle.randn((embedding_size,)) * scale)
  
      self.weight = self.W
  
file_path: PaddleHub/modules/image/text_to_image/stable_diffusion_waifu/diffusers/models/embeddings.py
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new GaussianFourierProjection object from inherited base classes: nn with example: obj = GaussianFourierProjection(embedding_size, scale)